Instructions to use Reza2kn/corr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Reza2kn/corr with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Reza2kn/corr") model = AutoModelForSeq2SeqLM.from_pretrained("Reza2kn/corr", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Reza2kn/corr: direct link, hf CLI and curl.
- Browser
- Download file 1.54 kB
-
https://huggingface.co/Reza2kn/corr/resolve/main/README.md
- Command line
-
hf download hf://Reza2kn/corr/README.md
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curl -L -o README.md https://huggingface.co/Reza2kn/corr/resolve/main/README.md
1.54 kB
metadata
library_name: transformers
license: apache-2.0
base_model: google/mt5-small
tags:
- generated_from_trainer
model-index:
- name: corr
results: []
corr
This model is a fine-tuned version of google/mt5-small on the None dataset.
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 0.05
- num_epochs: 6
Training results
Framework versions
- Transformers 5.13.0
- Pytorch 2.12.1+cu130
- Datasets 5.0.0
- Tokenizers 0.22.2
Licensing and attribution
This model distribution is licensed under the Apache License, Version 2.0. See LICENSE. Existing third-party copyright, license, and attribution notices remain applicable.
- Upstream: google/mt5-small; declared license:
apache-2.0.